DETAILED ACTION
The following Non-Final office action is in response to application 18/950,372 filed on 11/18/2024. Examiner notes priority claim to application 63/603,353 filed 11/28/2023. IDS filed 11/18/2024 has been considered.
Status of Claims
Claims 1-20 are currently pending and have been rejected as follows.
Claim Objections
Claims 1 and 14 are objected to because of the following informalities:
Claims 1, 14 recite: “determine an assignment assigning each of the autonomous vehicles of the fleet of autonomous vehicles to one of the plurality of predefined states” appearing to be a typographical error.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, system). Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-13 are directed toward the statutory category of a process (reciting a “method”). Claims 14-20 are directed toward the statutory category of a machine (reciting a “system”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1 and 14 are directed to an abstract idea by reciting identifying, … , a plurality of inputs including a current demand for services, predictions about future demand for services, and a current status of the fleet of autonomous vehicles including information identifying one of a plurality of predefined states for each autonomous vehicle of the fleet of autonomous vehicles; determining, …, a schedule based on the plurality of inputs, wherein the schedule defines a number of autonomous vehicles of the fleet of autonomous vehicles that are predicted to be in each of a plurality of expected future states; using, …, the schedule to determine an assignment assigning each of the autonomous vehicles of the fleet of autonomous vehicles to one of the plurality of predefined states; and […] (Example Claim 1).
The claims are considered abstract because these steps recite mathematical concepts like mathematical relationships; certain methods of organizing human activity like commercial interactions and managing interactions between people; and mental processes. The claims recite steps for identifying a plurality of inputs, determining a schedule based on those inputs, using the determined schedule to determine vehicle assignments, and sending an instruction to a vehicle to change its behavior. It is understood that the claimed steps aim to optimize the schedules for autonomous vehicles to reduce scheduling inefficiencies (Applicant’s Specification, [0017]-[0018]). By this evidence, the claims recite a type of mathematical concepts like mathematical relationships; certain methods of organizing human activity like commercial interactions and managing interactions between people; and mental processes common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., fleet management for autonomous vehicles).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as one or more processors; sending, by the one or more processors, to one of the autonomous vehicles of the fleet of autonomous vehicles an instruction to update the current status of the one of the autonomous vehicles based on the assignments, the instruction configured to cause the one of the autonomous vehicles of the fleet of autonomous vehicles to automatically change a behavior of the one of the autonomous vehicles of the fleet of autonomous vehicles) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)). The claimed “sending” limitation is insignificant extra-solution activity implementing the abstract idea.
Dependent claims 2-13 and 15-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps to “identifying” a plurality of inputs, “determining” a schedule, “using” the schedule for assignments, and “sending” an instruction to change behavior based on an assignment (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-20 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 USC 103 as being unpatentable over the teachings of
Bristow et al., US 20210192405 A1, hereinafter Bristow, in view of
Colijn et al., US 20220004200 A1, hereinafter Colijn. As per,
Claims 1, 14
Bristow teaches
A method for managing a fleet of autonomous vehicles of a transportation service, the method comprising: /
A system for managing a fleet of autonomous vehicles of a transportation service, the system comprising one or more processors configured to:
identifying, by one or more processors, a plurality of inputs including a current demand for services, predictions about future demand for services, and a current status of the fleet of autonomous vehicles including information identifying one of a plurality of predefined states for each autonomous vehicle of the fleet of autonomous vehicles; (Bristow [0055] “data is gathered from a plurality of data sources, which may include the demand data 501, the operations data 503, the environment data 505, the external constraints 507, the maintenance data 509, and the vehicle data 511;” [0056] “the data gathered in step 701 is analyzed and used to predict future conditions. The future conditions may include the predicted demand 513, the predicted fleet performance 515, and the predicted maintenance 517” note the inputs including current demand, predicted demand, and predicted maintenance/performance corresponding to the vehicle status)
determining, by the one or more processors, a schedule based on the plurality of inputs, wherein the schedule defines a number of autonomous vehicles of the fleet of autonomous vehicles that are predicted to be in each of a plurality of expected future states; (Bristow [0041] “The scheduling layer 405 may include user-defined constraints 519, user-defined parameters 521, and a goal-seeking algorithm 523. The monitored data … may be input into the goal-seeking algorithm, which may then generate and update the master schedule 106” note the master schedule derived from the plurality of inputs; [0044] “The master schedule 106 may include vehicle positions, flight schedules, maintenance schedules, recharging/refueling schedules … The master schedule 106 may provide maintenance schedules which intelligently sequence vehicle maintenance downtime so that a limited number of the vehicles 112 are down at any given time and downtime is scheduled in times of low demand” note the number of vehicles predicted to be available and unavailable)
using, by the one or more processors, the schedule to determine an assignment assigning each of the autonomous vehicles of the fleet of autonomous vehicles to one of the plurality of predefined states; and (Bristow [0065] “The master schedule 106 may include positions for the vehicles 112, flight plans, maintenance schedules, service schedules (e.g., schedules for refueling, recharging, and the like) … Once the master schedule 106 is generated, the master schedule 106 may be used to control the vehicles 112. In some embodiments, the master schedule 106 may be sent to the fleet controller 108. The fleet controller 108 controls the vehicles 112 to execute the master schedule 106” note the vehicles assigned to one of a plurality of predefined states)
[…].
Bristow does not explicitly teach, Colijn however in the analogous art of autonomous vehicle fleet management teaches
sending, by the one or more processors, to one of the autonomous vehicles of the fleet of autonomous vehicles an instruction to update the current status of the one of the autonomous vehicles based on the assignments, the instruction configured to cause the one of the autonomous vehicles of the fleet of autonomous vehicles to automatically change a behavior of the one of the autonomous vehicles of the fleet of autonomous vehicles. (Colijn [0005] “sending an instruction to the fleet, after sending the at least one assignment, thereby causing all vehicles of the subset to travel to a corresponding respective assigned parking location according to the at least one assignment” note the sending to a vehicle an instruction to update its current status based on an assignment causing the vehicle to change from its assigned behavior)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Bristow’s autonomous vehicle fleet management system to include sending an instruction after assignment that changes the behavior of the autonomous vehicle in view of Colijn in an effort to efficiently manage individual needs of vehicles within a fleet (see Colijn ¶ [0033] & MPEP 2143G).
Claims 2, 15
Bristow teaches
optimizing the schedule for a variable of interest. (Bristow [0062] “parameters used by the goal-seeking algorithm 523 in optimizing the master schedule 106. The parameters may be used to weight various goals in the goal-seeking algorithm” note the optimizing of the schedule with user definable weights assigned to variables)
Claim 3
Bristow teaches
wherein the variable of interest includes a number of trips. (Bristow [0062] “The parameters may be used to weight various goals in the goal-seeking algorithm, such as … number of flights completed” corresponding to trips)
Claim 4
Bristow does not explicitly teach, Colijn however in the analogous art of autonomous vehicle fleet management teaches
wherein the plurality of inputs further includes a number of depot areas for the transportation service, locations for the depot areas, and statuses of the depot areas. (Colijn [0005] “a corresponding respective assigned parking location … the plurality of factors include a number of spaces available at each parking location of the plurality of parking locations … the plurality of factors include whether any vehicles of the subset will reach a given parking location at a rate which is greater than an intake bandwidth of the given parking location” note the assigned parking locations corresponding to the depots and also note the space available corresponding to the depot status)
The motivation/rationale to combine Bristow with Colijn persists.
Claim 5
Bristow teaches
wherein the current demand for services includes a number of users who currently have an application for the transportation service open and have identified a destination. (Bristow [0033] “The demand data 501 may be collected from user inputs, third party systems, and the like. For example, users may schedule transportation of a payload or passenger from one location to another location through a booking application or the like” noting the scheduling of transportation service through a booking application with a destination)
Claim 6
Bristow teaches
determining the future demand for services based on historical demand for transportation service. (Bristow [0057] “The prediction layer 403 may generate the predicted demand 513 based on the demand data 501, the environment data 505, and any other relevant data. The demand data 501 may include direct demand, such as user requests for transportation and the like, and indirect demand, such as historical demand” note the historical demand relied on for predicted future demand)
Claims 7, 16
Bristow teaches
wherein determining the schedule includes determining expected future states for a plurality of timesteps. (Bristow [0031] “the predicted data from the prediction layer 403 to generate the master schedule 106, which is used to schedule positions, flight plans, maintenance schedules, refueling/recharging, and the like for the vehicles 112” note the prediction data used to determine the master schedule including scheduling of vehicle positions, maintenance schedules, and refueling/recharging schedules)
Claims 8, 17
Bristow teaches
evaluating performance of the transportation system by comparing the expected future states for the plurality of timesteps to actual states of the autonomous vehicles of the fleet of autonomous vehicles at corresponding times. (Bristow [0065] “Any data related to deviations from the master schedule may be sent to the fleet scheduler 104 and the master schedule 106 may be updated accordingly” note the deviations data collected for the master schedule, which includes the predicted future states of the fleet of vehicles)
Claims 9, 18
Bristow teaches
wherein the schedule further defines a number of autonomous vehicles of the fleet of autonomous vehicles that should be in each of the plurality of expected future states for each of the plurality of timesteps. (Bristow [0044] “The master schedule 106 may provide maintenance schedules which intelligently sequence vehicle maintenance downtime so that a limited number of the vehicles 112 are down at any given time”)
Claim 10
Bristow teaches
wherein determining the schedule includes determining when an autonomous vehicle of the fleet of autonomous vehicles will require maintenance. (Bristow [0056] “The future conditions may include … the predicted maintenance 517”)
Claims 11, 19
Bristow teaches
wherein determining the schedule includes determining an amount of time that each autonomous vehicle of the fleet of autonomous vehicles will remain in a current state for that autonomous vehicle of the fleet of autonomous vehicles. (Bristow [0044] “Entries for flights in the master schedule 106 may include a start location, an end location, a flight path, any stops along the flight path, expected duration” note the expected duration corresponding to the time a vehicle will remain in a current state)
Claim 12
Bristow teaches
wherein using the schedule to determine the assignment includes using a plurality of downstream models arranged in a hierarchy. (Bristow fig. 1 noting the fleet scheduler feeding the master schedule, master schedule feeding the fleet controller, and the fleet controller providing feedback to the fleet scheduler; see also [0055]-[0056]; [0065] from above)
Claim 13
Bristow teaches
using output of the plurality of downstream models to identify an updated plurality of inputs in order to perform a second iteration of determining a schedule. (Bristow [0041] “generate and update the master schedule 106;” [0056] “The prediction layer 403 generates predictive models based on the gathered data and constantly updates and maintains the predictive models based on new data that is gathered;” [0065] “Any data related to deviations from the master schedule may be sent to the fleet scheduler 104 and the master schedule 106 may be updated accordingly” note the new monitored data used to update predictive models, which then update the schedule; also note any deviations fed back to the fleet scheduler and the master schedule being updated again)
Claim 20
Bristow teaches
further comprising the fleet of autonomous vehicles. (Bristow [0014] “Embodiments of a fleet management system are described herein, with the fleet management system providing control and monitoring of autonomous vehicles”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. WO 2021076686 A1; US 20220309927 A1; Dong et al., Collaborative Autonomous Driving: Vision and Challenges, 2020.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624